Source-free Domain Adaptation. One of the constraints of SITA is that we cannot use the source data while adaptation, but only use the trained source model. Recent works in literature tackle this problem, but using an unlabeled target set for adaptation, with the hypothesis that the test instances are drawn from the target distribution. These methods involve - entropy minimization with divergence maximization [1]}, pseudo-labeling with self-reconstruction [2]}, as well as generating additional target images [3]}, [4]} and robustness to dropout [5]}. Unlike these methods, in SITA, we do not have any target set to adapt and do not have any assumption over the distribution of the test instances.
